Overview

Dataset statistics

Number of variables27
Number of observations110148
Missing cells478
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.7 MiB
Average record size in memory216.0 B

Variable types

NUM11
CAT10
BOOL5
DATE1

Reproduction

Analysis started2020-07-27 09:59:44.382778
Analysis finished2020-07-27 10:00:25.619042
Duration41.24 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

to_young_warn has constant value "373" Constant
app_month is highly correlated with client_id and 1 other fieldsHigh correlation
client_id is highly correlated with app_month and 1 other fieldsHigh correlation
days_from_CD is highly correlated with client_id and 1 other fieldsHigh correlation
client_id has unique values Unique
decline_app_cnt has 91471 (83.0%) zeros Zeros
bki_request_cnt has 28908 (26.2%) zeros Zeros

Variables

client_id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count110148
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55074.5
Minimum1
Maximum110148
Zeros0
Zeros (%)0.0%
Memory size860.5 KiB
2020-07-27T13:00:25.730666image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5508.35
Q127537.75
median55074.5
Q382611.25
95-th percentile104640.65
Maximum110148
Range110147
Interquartile range (IQR)55073.5

Descriptive statistics

Standard deviation31797.13306
Coefficient of variation (CV)0.5773476484
Kurtosis-1.2
Mean55074.5
Median Absolute Deviation (MAD)27537
Skewness0
Sum6066346026
Variance1011057671
2020-07-27T13:00:25.849277image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
20471< 0.1%
 
975411< 0.1%
 
934471< 0.1%
 
709201< 0.1%
 
729691< 0.1%
 
668261< 0.1%
 
688751< 0.1%
 
791161< 0.1%
 
811651< 0.1%
 
750221< 0.1%
 
Other values (110138)110138> 99.9%
 
ValueCountFrequency (%) 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
51< 0.1%
 
ValueCountFrequency (%) 
1101481< 0.1%
 
1101471< 0.1%
 
1101461< 0.1%
 
1101451< 0.1%
 
1101441< 0.1%
 
Distinct count120
Unique (%)0.1%
Missing0
Missing (%)0.0%
Memory size860.5 KiB
Minimum2014-01-01 00:00:00
Maximum2014-04-30 00:00:00
2020-07-27T13:00:27.198505image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-27T13:00:28.435399image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram

education
Categorical

Distinct count5
Unique (%)< 0.1%
Missing478
Missing (%)0.4%
Memory size860.5 KiB
SCH
57998
GRD
34768
UGR
14748
PGR
 
1865
ACD
 
291
ValueCountFrequency (%) 
SCH5799852.7%
 
GRD3476831.6%
 
UGR1474813.4%
 
PGR18651.7%
 
ACD2910.3%
 
(Missing)4780.4%
 
2020-07-27T13:00:29.328517image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

sex
Categorical

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size860.5 KiB
F
61836
M
48312
ValueCountFrequency (%) 
F6183656.1%
 
M4831243.9%
 
2020-07-27T13:00:29.842039image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

age
Real number (ℝ≥0)

Distinct count52
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.249409884882155
Minimum21
Maximum72
Zeros0
Zeros (%)0.0%
Memory size860.5 KiB
2020-07-27T13:00:29.952110image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile24
Q130
median37
Q348
95-th percentile60
Maximum72
Range51
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.51806263
Coefficient of variation (CV)0.2934582371
Kurtosis-0.7260121183
Mean39.24940988
Median Absolute Deviation (MAD)9
Skewness0.4802480831
Sum4323244
Variance132.6657668
2020-07-27T13:00:30.060193image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3140843.7%
 
2840353.7%
 
3040353.7%
 
2739643.6%
 
2939403.6%
 
2637803.4%
 
3237733.4%
 
3435483.2%
 
3334993.2%
 
3533863.1%
 
Other values (42)7210465.5%
 
ValueCountFrequency (%) 
2112621.1%
 
2214151.3%
 
2322952.1%
 
2427802.5%
 
2532923.0%
 
ValueCountFrequency (%) 
722< 0.1%
 
716< 0.1%
 
70600.1%
 
691100.1%
 
682610.2%
 

car
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size860.5 KiB
N
74290
Y
35858
ValueCountFrequency (%) 
N7429067.4%
 
Y3585832.6%
 

car_type
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size860.5 KiB
N
89140
Y
21008
ValueCountFrequency (%) 
N8914080.9%
 
Y2100819.1%
 

decline_app_cnt
Real number (ℝ≥0)

ZEROS

Distinct count24
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2732051421723499
Minimum0
Maximum33
Zeros91471
Zeros (%)83.0%
Memory size860.5 KiB
2020-07-27T13:00:30.178293image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum33
Range33
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.799099319
Coefficient of variation (CV)2.924905851
Kurtosis101.2380998
Mean0.2732051422
Median Absolute Deviation (MAD)0
Skewness6.493006696
Sum30093
Variance0.6385597216
2020-07-27T13:00:30.298412image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
09147183.0%
 
11250011.3%
 
236223.3%
 
313651.2%
 
46060.6%
 
52550.2%
 
61560.1%
 
7580.1%
 
837< 0.1%
 
929< 0.1%
 
Other values (14)49< 0.1%
 
ValueCountFrequency (%) 
09147183.0%
 
11250011.3%
 
236223.3%
 
313651.2%
 
46060.6%
 
ValueCountFrequency (%) 
331< 0.1%
 
301< 0.1%
 
241< 0.1%
 
221< 0.1%
 
211< 0.1%
 

good_work
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size860.5 KiB
0
91917
1
 
18231
ValueCountFrequency (%) 
09191783.4%
 
11823116.6%
 

score_bki
Real number (ℝ)

Distinct count102618
Unique (%)93.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.904535048828939
Minimum-3.62458632
Maximum0.19977285
Zeros0
Zeros (%)0.0%
Memory size860.5 KiB
2020-07-27T13:00:30.447572image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-3.62458632
5-th percentile-2.696247185
Q1-2.26043367
median-1.92082293
Q3-1.567888152
95-th percentile-1.055049083
Maximum0.19977285
Range3.82435917
Interquartile range (IQR)0.6925455175

Descriptive statistics

Standard deviation0.4993974924
Coefficient of variation (CV)-0.2622149131
Kurtosis-0.1492918934
Mean-1.904535049
Median Absolute Deviation (MAD)0.34574209
Skewness0.1939872976
Sum-209780.7266
Variance0.2493978554
2020-07-27T13:00:30.551677image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.775262795170.5%
 
-2.10421094540.4%
 
-2.225003634240.4%
 
-2.169663783750.3%
 
-2.024100052780.3%
 
-1.920822932700.2%
 
-2.387268042380.2%
 
-1.526421942070.2%
 
-2.447238992070.2%
 
-2.27294091760.2%
 
Other values (102608)10700297.1%
 
ValueCountFrequency (%) 
-3.624586321< 0.1%
 
-3.597980831< 0.1%
 
-3.582586911< 0.1%
 
-3.574197081< 0.1%
 
-3.564224061< 0.1%
 
ValueCountFrequency (%) 
0.199772852< 0.1%
 
0.19806991< 0.1%
 
0.188820441< 0.1%
 
0.183612971< 0.1%
 
0.168549331< 0.1%
 

bki_request_cnt
Real number (ℝ≥0)

ZEROS

Distinct count40
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0050023604604714
Minimum0
Maximum53
Zeros28908
Zeros (%)26.2%
Memory size860.5 KiB
2020-07-27T13:00:30.669784image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6
Maximum53
Range53
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.266925867
Coefficient of variation (CV)1.130635012
Kurtosis23.16785082
Mean2.00500236
Median Absolute Deviation (MAD)1
Skewness3.082728152
Sum220847
Variance5.138952887
2020-07-27T13:00:30.772846image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
02890826.2%
 
12729524.8%
 
22048118.6%
 
31367012.4%
 
484067.6%
 
549604.5%
 
625002.3%
 
712921.2%
 
87350.7%
 
94590.4%
 
Other values (30)14421.3%
 
ValueCountFrequency (%) 
02890826.2%
 
12729524.8%
 
22048118.6%
 
31367012.4%
 
484067.6%
 
ValueCountFrequency (%) 
531< 0.1%
 
471< 0.1%
 
461< 0.1%
 
451< 0.1%
 
411< 0.1%
 

region_rating
Real number (ℝ≥0)

Distinct count7
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.7511893089298
Minimum20
Maximum80
Zeros0
Zeros (%)0.0%
Memory size860.5 KiB
2020-07-27T13:00:30.885977image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile40
Q150
median50
Q360
95-th percentile80
Maximum80
Range60
Interquartile range (IQR)10

Descriptive statistics

Standard deviation13.06592289
Coefficient of variation (CV)0.2302317017
Kurtosis-0.6334345368
Mean56.75118931
Median Absolute Deviation (MAD)10
Skewness0.4778692262
Sum6251030
Variance170.7183409
2020-07-27T13:00:30.999049image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
504098137.2%
 
602399921.8%
 
401794716.3%
 
801717015.6%
 
7093048.4%
 
304340.4%
 
203130.3%
 
ValueCountFrequency (%) 
203130.3%
 
304340.4%
 
401794716.3%
 
504098137.2%
 
602399921.8%
 
ValueCountFrequency (%) 
801717015.6%
 
7093048.4%
 
602399921.8%
 
504098137.2%
 
401794716.3%
 

home_address
Categorical

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size860.5 KiB
2
59591
1
48688
3
 
1869
ValueCountFrequency (%) 
25959154.1%
 
14868844.2%
 
318691.7%
 
2020-07-27T13:00:31.515107image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

work_address
Categorical

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size860.5 KiB
3
67113
2
30761
1
 
12274
ValueCountFrequency (%) 
36711360.9%
 
23076127.9%
 
11227411.1%
 
2020-07-27T13:00:31.998117image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

income
Real number (ℝ≥0)

Distinct count1207
Unique (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41012.648536514505
Minimum1000
Maximum1000000
Zeros0
Zeros (%)0.0%
Memory size860.5 KiB
2020-07-27T13:00:32.112192image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile10000
Q120000
median30000
Q348000
95-th percentile100000
Maximum1000000
Range999000
Interquartile range (IQR)28000

Descriptive statistics

Standard deviation45399.73505
Coefficient of variation (CV)1.10696911
Kurtosis100.1746159
Mean41012.64854
Median Absolute Deviation (MAD)12000
Skewness7.503020095
Sum4517461211
Variance2061135943
2020-07-27T13:00:32.223973image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
30000104379.5%
 
2500090908.3%
 
2000081747.4%
 
4000073836.7%
 
5000067426.1%
 
3500063195.7%
 
1500058745.3%
 
6000038183.5%
 
4500036703.3%
 
1800027322.5%
 
Other values (1197)4590941.7%
 
ValueCountFrequency (%) 
10006< 0.1%
 
11001< 0.1%
 
12001< 0.1%
 
15002< 0.1%
 
17001< 0.1%
 
ValueCountFrequency (%) 
100000013< 0.1%
 
9999994< 0.1%
 
9990002< 0.1%
 
9900001< 0.1%
 
9500004< 0.1%
 

sna
Categorical

Distinct count4
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size860.5 KiB
1
70681
4
17481
2
15832
3
 
6154
ValueCountFrequency (%) 
17068164.2%
 
41748115.9%
 
21583214.4%
 
361545.6%
 
2020-07-27T13:00:32.724489image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

first_time
Categorical

Distinct count4
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size860.5 KiB
3
46588
4
28017
1
18296
2
17247
ValueCountFrequency (%) 
34658842.3%
 
42801725.4%
 
11829616.6%
 
21724715.7%
 
2020-07-27T13:00:33.214865image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size860.5 KiB
N
93721
Y
 
16427
ValueCountFrequency (%) 
N9372185.1%
 
Y1642714.9%
 

default
Categorical

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size860.5 KiB
0
64427
-1
36349
1
 
9372
ValueCountFrequency (%) 
06442758.5%
 
-13634933.0%
 
193728.5%
 
2020-07-27T13:00:33.730935image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length2
Median length1
Mean length1.330001453
Min length1

train
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size860.5 KiB
1
73799
0
36349
ValueCountFrequency (%) 
17379967.0%
 
03634933.0%
 

car1
Categorical

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size860.5 KiB
0
74290
2
21008
1
 
14850
ValueCountFrequency (%) 
07429067.4%
 
22100819.1%
 
11485013.5%
 
2020-07-27T13:00:34.232975image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

app_month
Categorical

HIGH CORRELATION

Distinct count4
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size860.5 KiB
3
31597
2
27097
4
26266
1
25188
ValueCountFrequency (%) 
33159728.7%
 
22709724.6%
 
42626623.8%
 
12518822.9%
 
2020-07-27T13:00:34.722462image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

score_bki_income
Real number (ℝ≥0)

Distinct count106475
Unique (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.025660981485143224
Minimum0.000521989089
Maximum0.7445010830000001
Zeros0
Zeros (%)0.0%
Memory size860.5 KiB
2020-07-27T13:00:34.865604image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0.000521989089
5-th percentile0.006124594341
Q10.0135169247
median0.02093235222
Q30.03179510595
95-th percentile0.06150379223
Maximum0.744501083
Range0.7439790939
Interquartile range (IQR)0.01827818125

Descriptive statistics

Standard deviation0.01991195944
Coefficient of variation (CV)0.7759625035
Kurtosis70.34170447
Mean0.02566098149
Median Absolute Deviation (MAD)0.008542532441
Skewness4.309870426
Sum2826.505789
Variance0.0003964861286
2020-07-27T13:00:34.973686image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.030724808755< 0.1%
 
0.020483205851< 0.1%
 
0.025895639448< 0.1%
 
0.0323695492546< 0.1%
 
0.0240966760445< 0.1%
 
0.0200805633745< 0.1%
 
0.0210944990344< 0.1%
 
0.0301208450544< 0.1%
 
0.021579699544< 0.1%
 
0.0161847746343< 0.1%
 
Other values (106465)10968399.6%
 
ValueCountFrequency (%) 
0.0005219890891< 0.1%
 
0.0005367640931< 0.1%
 
0.0005540298531< 0.1%
 
0.0005679507261< 0.1%
 
0.0005693250351< 0.1%
 
ValueCountFrequency (%) 
0.7445010831< 0.1%
 
0.6778668361< 0.1%
 
0.6225072591< 0.1%
 
0.62137911< 0.1%
 
0.5896120891< 0.1%
 

days_from_CD
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count120
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2130.0285343356213
Minimum2072
Maximum2191
Zeros0
Zeros (%)0.0%
Memory size860.5 KiB
2020-07-27T13:00:35.082811image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum2072
5-th percentile2079
Q12102
median2129
Q32158
95-th percentile2180
Maximum2191
Range119
Interquartile range (IQR)56

Descriptive statistics

Standard deviation32.07607842
Coefficient of variation (CV)0.01505899001
Kurtosis-1.141261894
Mean2130.028534
Median Absolute Deviation (MAD)28
Skewness0.007687821604
Sum234618383
Variance1028.874807
2020-07-27T13:00:35.185897image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
211514911.4%
 
211413631.2%
 
211613501.2%
 
210213171.2%
 
209512961.2%
 
210012911.2%
 
212212451.1%
 
212912421.1%
 
210112391.1%
 
215012331.1%
 
Other values (110)9708188.1%
 
ValueCountFrequency (%) 
20728650.8%
 
20735460.5%
 
20748780.8%
 
20754970.5%
 
20766430.6%
 
ValueCountFrequency (%) 
2191560.1%
 
21902040.2%
 
21893130.3%
 
21884470.4%
 
21874250.4%
 

score_bki_region_rating
Real number (ℝ)

Distinct count104371
Unique (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.470444237018258
Minimum-15.97430457142857
Maximum0.8026158571428571
Zeros0
Zeros (%)0.0%
Memory size860.5 KiB
2020-07-27T13:00:35.339516image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-15.97430457
5-th percentile-5.550553024
Q1-4.23608926
median-3.375094574
Q3-2.60611246
95-th percentile-1.664678955
Maximum0.8026158571
Range16.77692043
Interquartile range (IQR)1.629976801

Descriptive statistics

Standard deviation1.22549637
Coefficient of variation (CV)-0.3531237751
Kurtosis2.62070108
Mean-3.470444237
Median Absolute Deviation (MAD)0.8101621287
Skewness-0.7572653356
Sum-382262.4918
Variance1.501841353
2020-07-27T13:00:35.449158image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-4.1259037251760.2%
 
-3.4809074311700.2%
 
-4.3627522161530.1%
 
-2.1916824571110.1%
 
-4.2542427061090.1%
 
-3.4495260661060.1%
 
-2.9102668691010.1%
 
-3.9688236271000.1%
 
-4.6809177251000.1%
 
-3.647546934960.1%
 
Other values (104361)10892698.9%
 
ValueCountFrequency (%) 
-15.974304571< 0.1%
 
-15.400417431< 0.1%
 
-15.381817811< 0.1%
 
-14.597377191< 0.1%
 
-14.462572671< 0.1%
 
ValueCountFrequency (%) 
0.80261585711< 0.1%
 
0.48725085371< 0.1%
 
0.39171147061< 0.1%
 
0.32470475411< 0.1%
 
0.30954170491< 0.1%
 

delta_app
Real number (ℝ≥0)

Distinct count78548
Unique (%)71.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0977638370944993
Minimum0.0
Maximum5.254020844302736
Zeros1
Zeros (%)< 0.1%
Memory size860.5 KiB
2020-07-27T13:00:35.587287image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.9731908536
Q11
median1.069635614
Q31.151407113
95-th percentile1.319390962
Maximum5.254020844
Range5.254020844
Interquartile range (IQR)0.1514071127

Descriptive statistics

Standard deviation0.1250967674
Coefficient of variation (CV)0.1139559924
Kurtosis22.93969712
Mean1.097763837
Median Absolute Deviation (MAD)0.0696356142
Skewness2.306375339
Sum120916.4911
Variance0.01564920122
2020-07-27T13:00:35.688348image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
12871026.1%
 
1.0680830731190.1%
 
1.0543214151180.1%
 
1.0643869261090.1%
 
1.0663897241020.1%
 
1.058775422780.1%
 
1.061935607780.1%
 
1.073048215740.1%
 
1.10864283610.1%
 
1.12877385153< 0.1%
 
Other values (78538)8064673.2%
 
ValueCountFrequency (%) 
01< 0.1%
 
0.062626517531< 0.1%
 
0.2069194721< 0.1%
 
0.24690566931< 0.1%
 
0.28832955911< 0.1%
 
ValueCountFrequency (%) 
5.2540208441< 0.1%
 
4.0056218751< 0.1%
 
2.7731297971< 0.1%
 
2.6556469021< 0.1%
 
2.6454221781< 0.1%
 

to_young_warn
Categorical

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size860.5 KiB
373
110148
ValueCountFrequency (%) 
373110148100.0%
 
2020-07-27T13:00:36.182768image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Interactions

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2020-07-27T13:00:22.823784image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2020-07-27T13:00:36.331367image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-07-27T13:00:36.639610image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-07-27T13:00:36.938877image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-07-27T13:00:37.250648image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-07-27T13:00:37.535918image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-07-27T13:00:23.321545image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-27T13:00:24.541444image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-27T13:00:25.257795image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

client_idapp_dateeducationsexagecarcar_typedecline_app_cntgood_workscore_bkibki_request_cntregion_ratinghome_addresswork_addressincomesnafirst_timeforeign_passportdefaulttraincar1app_monthscore_bki_incomedays_from_CDscore_bki_region_ratingdelta_appto_young_warn
0259052014-02-01SCHM62YY00-2.008753150121800041N01220.0346692160-3.9387311.061466373
1631612014-03-12SCHF59NN00-1.532276350231900041N01030.0353522121-3.0044631.140659373
2258872014-02-01SCHM25YN20-1.408142180123000014Y01120.0228032160-1.7384470.956912373
3162222014-01-23SCHF53NN00-2.057471250231000013N01010.0619172169-4.0342581.125913373
41016552014-04-18GRDM48NN01-1.244723160233000014Y01040.0233482084-2.0405291.038087373
5414152014-02-18SCHM27YN01-2.032257050111500023N01120.0414462143-3.9848181.000000373
6284362014-02-04SCHM39NN00-2.225004060122800011N01020.0215152157-3.6475471.000000373
7687692014-03-17SCHF39NN00-1.522739150234500033N01030.0149482116-2.9857631.046594373
8384242014-02-14SCHF50YN10-1.676061050113000014N01120.0219102147-3.2863940.948714373
944962014-01-10UGRF54NN00-2.695176150232400013N01010.0231422182-5.2846581.082470373

Last rows

client_idapp_dateeducationsexagecarcar_typedecline_app_cntgood_workscore_bkibki_request_cntregion_ratinghome_addresswork_addressincomesnafirst_timeforeign_passportdefaulttraincar1app_monthscore_bki_incomedays_from_CDscore_bki_region_ratingdelta_appto_young_warn
110138160722014-01-23GRDF28NN00-1.651781460121300013N-10010.0507492169-2.7078371.202172373
110139100902014-01-17SCHF53YN00-1.84505825012700011N-10110.0914872175-3.6177601.112914373
110140904352014-04-07UGRF48NN00-2.066300160112700014N-10040.0229002095-3.3873761.063227373
110141425092014-02-19SCHF58YY01-1.857117150232500043N-10220.0255682142-3.6414061.056826373
110142724052014-03-20SCHF40NN00-2.039905050232000041N-10030.0310462113-3.9998141.000000373
110143837752014-03-31SCHF37NN10-1.744976350231500041N-10030.0433612102-3.4215211.106789373
1101441062542014-04-25GRDF64YY00-2.2937813601220000014N-10240.0029782077-3.7602971.210563373
110145818522014-03-30GRDM31NN20-0.940752150126000042N-10030.0121812103-1.8446120.971214373
11014619712014-01-07UGRF27NN10-1.242392280233000011N-10010.0233562185-1.5338171.038016373
110147690442014-03-17SCHM38NN00-1.507549250121500042N-10030.0449442116-2.9559791.092259373